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Purpose – The purpose of this paper is to present an approach to evaluating contributions in collaborative authoring environments, and in particular, Wikis using social network measures. Design/methodology/approach – A social network model for Wikipedia has been constructed, and metrics of importance such as centrality have been defined. Data has been gathered from articles belonging to the same topic using a web crawler, in order to evaluate the outcome of the social network measures in the articles. Findings – Finds that the question of the reliability regarding Wikipedia content is a challenging one and as Wikipedia grows, the problem becomes more demanding, especially for topics with controversial views such as politics or history. Practical implications – It is believed that the approach presented here could be used to improve the authoritativeness of content found in Wikipedia and similar sources. Originality/value – This work tries to develop a network approach to the evaluation of Wiki contributions, and approaches the problem of quality Wikipedia content from a social network point of view

Person identification based on features extracted parametrically from the EEG spectrum is investigated in this work. The method proposed utilizes computational geometry algorithms (convex polygon intersections), appropriately modified, in order to classify unknown EEGs. The signal processing step includes EEG spectral analysis for feature extraction, by fitting a linear model of the AR type on the alpha rhythm EEG signal.The correct classification scores obtained on real EEG data experiments (91% in the worst case) are promising in that they corroborate existing evidence that EEG carries genetically specific information and is therefore appropriate as a basis for person identification methods.

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Person identification based on spectral information extracted from the EEG is addressed in this work a problem that has not yet been seen in a signal processing framework. Spectral features are extracted non-parametrically from real EEG data recorded from healthy individuals. Neural network classification is applied on these features using a Learning Vector Quantizer in an attempt to experimentally investigate the connection between a person's EEG and genetically specific information. The proposed method, compared with previously proposed methods, has yielded encouraging correct classification scores in the range of 80% to 100% (case-dependent). These results are in agreement with previous research showing evidence that the EEG carries genetic information.